[jira] [Created] (HADOOP-17789) S3 read performance with Spark with Hadoop 3.3.1 is slower than older Hadoop

["Arghya Saha (Jira)" <ji...@apache.org>]

Arghya Saha created HADOOP-17789:
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             Summary: S3 read performance with Spark with Hadoop 3.3.1 is slower than older Hadoop
                 Key: HADOOP-17789
                 URL: https://issues.apache.org/jira/browse/HADOOP-17789
             Project: Hadoop Common
          Issue Type: Improvement
    Affects Versions: 3.3.1
            Reporter: Arghya Saha


This is issue is continuation to https://issues.apache.org/jira/browse/HADOOP-17755

The input data reported by Spark(Hadoop 3.3.1) was almost double and read runtime also increased (around 20%) compared to Spark(Hadoop 3.2.0) with same exact amount of resource and same configuration. And this is happening with other jobs as well which was not impacted by read fully error as stated above.

*I was having the same exact issue when I was using the workaround  fs.s3a.readahead.range = 1G with Hadoop 3.2.0*

Below is further details :

 
|Hadoop Version|Actual size of the files(in SQL Tab)|Reported size of the file(In Stages)|Time to complete the Stage|fs.s3a.readahead.range|
|Hadoop 3.2.0|29.3 GiB|29.3 GiB|23 min|64K|
|Hadoop 3.3.1|29.3 GiB|*{color:#ff0000}58.7 GiB{color}*|*{color:#ff0000}27 min{color}*|{color:#172b4d}64K{color}|
|Hadoop 3.2.0|29.3 GiB|*{color:#ff0000}58.7 GiB{color}*|*{color:#ff0000}~27 min{color}*|{color:#172b4d}1G{color}|
 * *Shuffle Write* is same (95.9 GiB) for all the above three cases

I was expecting some improvement(or same as 3.2.0) with Hadoop 3.3.1 with read operations, please suggest how to approach this and resolve this.

I have used the default s3a config along with below and also using EKS cluster
{code:java}
spark.hadoop.fs.s3a.committer.magic.enabled: 'true'
spark.hadoop.fs.s3a.committer.name: magic
spark.hadoop.mapreduce.outputcommitter.factory.scheme.s3a: org.apache.hadoop.fs.s3a.commit.S3ACommitterFactory
spark.hadoop.fs.s3a.downgrade.syncable.exceptions: "true"{code}
 * I did not use 
{code:java}
spark.hadoop.fs.s3a.experimental.input.fadvise=random{code}

And as already mentioned I have used same Spark, same amount of resources and same config.  Only change is Hadoop 3.2.0 to Hadoop 3.3.1 (Built with Spark using ./dev/make-distribution.sh --name spark-patched --pip -Pkubernetes -Phive -Phive-thriftserver -Dhadoop.version="3.3.1")



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